Centre for Visual Computing, Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
Comput Methods Programs Biomed. 2012 Oct;108(1):186-96. doi: 10.1016/j.cmpb.2012.03.004. Epub 2012 Apr 30.
Diabetic retinopathy (DR) has become a serious threat in our society, which causes 45% of the legal blindness in diabetes patients. Early detection as well as the periodic screening of DR helps in reducing the progress of this disease and in preventing the subsequent loss of visual capability. This paper provides an automated diagnosis system for DR integrated with a user-friendly interface. The grading of the severity level of DR is based on detecting and analyzing the early clinical signs associated with the disease, such as microaneurysms (MAs) and hemorrhages (HAs). The system extracts some retinal features, such as optic disc, fovea, and retinal tissue for easier segmentation of dark spot lesions in the fundus images. That is followed by the classification of the correctly segmented spots into MAs and HAs. Based on the number and location of MAs and HAs, the system quantifies the severity level of DR. A database of 98 color images is used in order to evaluate the performance of the developed system. From the experimental results, it is found that the proposed system achieves 84.31% and 87.53% values in terms of sensitivity for the detection of MAs and HAs respectively. In terms of specificity, the system achieves 93.63% and 95.08% values for the detection of MAs and HAs respectively. Also, the proposed system achieves 68.98% and 74.91% values in terms of kappa coefficient for the detection of MAs and HAs respectively. Moreover, the system yields sensitivity and specificity values of 89.47% and 95.65% for the classification of DR versus normal.
糖尿病性视网膜病变（DR）已成为我们社会中的严重威胁，它导致糖尿病患者中有 45%的人合法失明。早期发现和定期筛查 DR 有助于减缓该疾病的进展，并防止随后视力丧失。本文提供了一个集成用户友好界面的 DR 自动诊断系统。DR 的严重程度分级是基于检测和分析与该疾病相关的早期临床迹象，如微动脉瘤（MAs）和出血（HAs）。该系统提取一些视网膜特征，如视盘、黄斑和视网膜组织，以便更容易对眼底图像中的暗斑病变进行分割。然后，将正确分割的斑点分类为 MAs 和 HAs。根据 MAs 和 HAs 的数量和位置，系统量化 DR 的严重程度。该系统使用了一个包含 98 张彩色图像的数据库来评估所开发系统的性能。从实验结果中发现，所提出的系统在检测 MAs 和 HAs 方面的灵敏度分别达到 84.31%和 87.53%。在特异性方面，系统在检测 MAs 和 HAs 方面的特异性分别达到 93.63%和 95.08%。此外，所提出的系统在检测 MAs 和 HAs 方面的kappa 系数分别达到 68.98%和 74.91%。此外，该系统在分类 DR 与正常方面的灵敏度和特异性值分别为 89.47%和 95.65%。
